1. Field of the Invention
The present invention generally relates to methods and systems for detecting defects in patterns formed on a specimen.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to drive higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Defect review typically involves re-detecting defects detected as such by an inspection process and generating additional information about the defects at a higher resolution using either a high magnification optical system or a scanning electron microscope (SEM). Defect review is therefore performed at discrete locations on the wafer where defects have been detected by inspection. The higher resolution data for the defects generated by defect review is more suitable for determining attributes of the defects such as profile, roughness, more accurate size information, etc. Since the defect review is performed for defects detected on the wafer by inspection, the parameters used for defect review at a location of a detected defect may be determined based on attributes of the defects determined by the inspection process.
Metrology processes are also used at various steps during a semiconductor manufacturing process to monitor and control the process. Metrology processes are different than inspection processes in that, unlike inspection processes in which defects are detected on a wafer, metrology processes are used to measure one or more characteristics of the wafer that cannot be determined using currently used inspection tools. For example, metrology processes are used to measure one or more characteristics of a wafer such as a dimension (e.g., line width, thickness, etc.) of features formed on the wafer during a process such that the performance of the process can be determined from the one or more characteristics. In addition, if the one or more characteristics of the wafer are unacceptable (e.g., out of a predetermined range for the characteristic(s)), the measurements of the one or more characteristics of the wafer may be used to alter one or more parameters of the process such that additional wafers manufactured by the process have acceptable characteristic(s).
Metrology processes are also different than defect review processes in that, unlike defect review processes in which defects that are detected by inspection are re-visited in defect review, metrology processes may be performed at locations at which no defect has been detected. In other words, unlike defect review, the locations at which a metrology process is performed on a wafer may be independent of the results of an inspection process performed on the wafer. In particular, the locations at which a metrology process is performed may be selected independently of inspection results. In addition, since locations on the wafer at which metrology is performed may be selected independently of inspection results, unlike defect review in which the locations on the wafer at which defect review is to be performed cannot be determined until the inspection results for the wafer are generated and available for use, the locations at which the metrology process is performed may be determined before an inspection process has been performed on the wafer.
Measuring critical dimensions (CDs) of “key” structures is vital to process monitoring in current and next generation nodes (e.g., 7 nm and 5 nm). The determination of “key” comes from several sources such as known rules of density and proximity, simulation, experience, and optical proximity correction (OPC) among others. However, a SEM image is able to see pattern fidelity irrespective of these priors and can help identify unknown “hot spots” without an explicit need for these priors, which can be extremely valuable for process control but also potentially open new characterization methodologies for pattern fidelities.
Currently used methods for CD-SEMs have several challenges such as that they are slow, they require careful setup for each site and a knowledge of which sites to measure, and their results need to be interpreted further downline. The use of substantially fast review SEMs is gaining popularity to cover this fidelity application. In addition, it allows customers to develop and use their own algorithm solutions reducing these platforms as “image takers.” Therefore, the need is clear to overcome these challenges to enable adequate pattern fidelity monitoring for users.
Typical CD measurement applications include a number of modules. One such module is region of interest (ROI) definition that includes identification and marking of areas where CD measurements are to he taken. Another such module includes an optional design rendering step which includes generating expected SEM contours from pre-OPC design. An additional module includes edge extraction that includes generating edges and/or contours from current SEM images. A further such module includes measurement algorithms that include comparing expected and current “edges” within the defined ROI.
There have been several previous attempts to cover these steps. The main challenges of these previous attempts lie in heuristic determination of contours which can fail for complex patterns and pattern intersections. These attempts also lack robustness to imaging conditions and require parameter tweaking for noisy and soft images. Therefore, any method must overcome these limitations as much as possible if we are to generically explore any random pattern for pattern fidelity.
For pattern hot spot detection, currently used methods employ both learning and non-learning based methods (i.e., use hand crafted algorithms). Some of these methods try to detect hot spots using learning based algorithms with CAD data only (no images) as input and trying to predict/classify whether a given pattern is a hot spot or not. Other methods use both CAD and image (SEM or optical) data and predict/classify whether a detected defect is a hot spot or not manually or using hand crafted features or learning based features. However, none of these methods quantify these hot spots by reporting a CD metric which is required in order to accurately determine a hot spot based on the user-defined threshold.
The currently used methods for hot spot detection have a number of disadvantages. For example, the currently used methods have no flexibility to automatically adapt to different pattern types (i.e., memory or logic). In addition, the currently used methods have no generalization to different image modalities. In an additional example, the currently used methods require hand crafted (heuristics) models of image modalities to characterize pattern variation and bias. In a further example, the currently used methods provide no quantitative pattern characterization and hot spot detection. Instead, the currently used methods report CD or other pattern fidelity metrics for the entire field of view from a single shot measurement. In yet another example, the currently used methods provide no ability to handle OPC errors naturally without having to handle each kind of OPC error heuristically.
Accordingly, it would be advantageous to develop systems and methods for detecting defects in patterns formed on a specimen that do not have one or more of the disadvantages described above.